4. ASSESSMENT OF AGRICULTURAL VULNERABILITY USING NDVI DATASETS
AVHRR bi-monthly NDVI images were sub-set, downloaded and stacked and pre-processed. Pixel-wise Max NDVI was
identified to arrive at Maximum Greenness for a given pixel during corresponding year for the study period 1982-2006. This
was followed by estimation of mean and standard deviation for Max NDVI. To understand variability in Greenness as an
indicator of agricultural vulnerability, coefficient of variation CV of Max NDVI was calculated which formed the basis for
vulnerability analysis. The output helped in identifying regional agricultural vulnerability. Due to coarse resolution of dataset,
agricultural vulnerability was identified at agro-ecological sub- regions AESR level. These regions are vulnerable due to low
rainfall, frequent drought due to high rainfall variability and a gradual drying phase. Study indicated that a conservative
estimate of 29 mha or 20.4 of net sown area in India was vulnerable to climate change.
CV of annual Max NDVI was estimated to assess trend in NDVI variability across various states and AESR in the
country. In arid regions in western Rajasthan and Gujarat and south-central India in arid districts of Bellary and Anantapur,
vegetation cover remainedsparse as agriculture is restricted to short window during southwest monsoon period. However the
large livestock population contributes to increased vulnerability as fodder availability could be critical in the event of drought.
Study of trend in Max NDVI during 1982-2006 Figure 3a b indicates a positive trend in vegetation index in this critical
zone. In semi-arid and sub-humid zones which account for large area under rained agriculture, the natural resource base
supporting agricultural enterprise is poor owing to shallow soil cover and falling groundwater table, in addition to presence of
large number of marginal and small farm holdings that depend on southwest monsoon rainfall for carrying out agricultural
operations. Agricultural vulnerability in this zone increases owing to impact of climate variability. In the humid regions
where agriculture is undertaken in two to three cropping seasons annually, floods andor drought could be devastating. Figure 3
a b indicates a declining trend in AVHRR NDVI in humid western coastal zone and the north-eastern region of India
which could be devastating for local and national economy. However, in the recent MODIS dataset, this was not seen,
except in a few districts in the aforesaid regions. MODIS bi- monthly 16 days NDVI dataset of 250m resolution for 2001-
2012 period was similarly processed and spatial variability of Greenness was identified at a larger scale namely district, which
is an administrative unit entrusted with implementation of policy at local-level. Study indicated that over 47 mha or 33
of net sown area was vulnerable to climate change. MODIS dataset was also used to study variability in length-of-crop-
growing period LGP as an indicator of sensitivity of agricultural vulnerability.
4.1. Study of variations in LGP imparting sensitivity to rainfed agriculture
As recommended by IPCC 2007, 2012 NDVI was used as an exposure indicator to study variations in LGP, considered a
sensitivity indicator.To study variations in LGP that contributes to agricultural vulnerability in a region, a method was
developed to identify Start-of-Season SOS and End-of-Season EOS for each AESR in India as indicated earlier. Data on
variations in LGP help in developing appropriate package of practices for crop variety selection and crop management
suitable for late onset or early withdrawal of monsoon or long intermittent break in rainfall during rainy season.Based on CV
of Max NDVI and SPI trends, variations of LGP in various AESR across the country was analysed. A methodology was
developed to study trends in variations in LGP in both Kharif and Rabi cropping seasons across India.Mann Kendall Test was
performed to analyse the trend in LGP derived from AVHRR and MODIS datasets. Out of 57 ASER excluding JK, LGP
showedan increasing positive trend in 17 AESR regions with 1 significance while in eight AESR the increase was
significant at 5 level. In 32 AESR, there was no significant trend. However 13 of these 32 AESR regions recorded declining
trend in LGP as indicated in Figure 4.
In arid agro-ecological region covering western Rajasthan, Kachchh in western Gujarat and Anantapur in Peninsular India,
there was no change in lower limit of LGP which denotes least number of days available for crop growth. However, there was a
decline in lower limit of LGP in sub-humid rain-shadow region located in Maharashtra and Karnataka besides Nellore-
Prakasam region in the state of Andhra Pradesh in addition to Madhya Pradesh and Chattisgarh in central India. In arid
Jaisalmer district, there was no change in LGP while in semi- arid districts of western Rajasthan and arid district of
Anantapur, there was an increase in LGP; in rest of India, there was a decrease in LGP that augurs hardship to rainfed farmers
in the region. Steep decrease in upper limit of LGP was noticed in prime agricultural areas in the states of Madhya Pradesh,
Maharashtra and Telangana that will hurt farming communities in the region.
Analysis of MODIS dataset indicated a larger extent of agriculturally vulnerable region in India owing to finer ground
resolution 250m compared to that of AVHRR. Itenabled a precise analysis and accurate estimation of extent of vulnerable
regions in India. There was dissimilarity in trends in lower limit of LGP as identified using MODIS and AVHRR datasets.
While the former indicated a rise in lower limit of LGP in several AESR in central and southern India and a decrease in
others during the period 2001-2012, the latter indicated no significant change in LGP Kaushalya et al. 2014.
Figure 3 a b. Trend in NDVI
5. MAPPING SPATIAL EXTENT OF AGRICULTURAL VULNERABILITY
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